System and method for generating shipment forecasts for materials handling facilities

ABSTRACT

Embodiments may include a forecasting component that, for each of different service levels offered to customers, generates a projection of the quantity of shipments to be shipped according to that service level during a time period. Based on an aggregate quantity of shipments projected to be shipped, projected quantities of shipments for the multiple service levels may be modified. The forecasting component may, for each service level, receive information specifying a distribution of different shipment methods that are projected to be utilized to ship shipments of a particular priority designation to meet requirements of that service level. Based on the distribution for each service level and the modified projected quantity of shipments for each service level, a forecast specifying quantities of shipments that are to be shipped during the time period according to each of the different shipment methods may be generated.

BACKGROUND

Electronic marketplaces, such as those accessible via the Internet, mayinclude a catalog of items or products available for purchase. Theseitems may be offered as the basis for commerce (e.g., sale or trade). Inone example, customers may utilize a web browser to visit a merchant'swebsite, select an item for purchase from the catalog, and engage in acheckout process to finalize an order for the item. The merchant mayoperate a fulfillment network including various facilities in order toprocess such orders. For instance, the merchant may operate a facilitythat prepares shipments of purchased items. A shipment carrier mayacquire such shipments from the merchant and deliver the shipments tothe respective purchasing customers.

Typically, varying workloads may require varying levels of resources forboth merchants and shipment carriers. For instance, if the quantity oforders processed by a facility within the merchant's fulfillment networkdramatically increases, the merchant may increase the quantity of laborwithin the facility in order to prevent processing delays. Similarly, ifthe quantity of orders processed by that facility substantiallydecreases, the merchant may decrease the quantity of labor within thefacility in order to avoid unnecessary expenditures on labor. Shipmentcarriers may also tailor their operations dependent upon the workload ofmerchant facilities. For example, if the quantity of outgoing shipmentsat a merchant facility substantially changes, the shipment carrier maychange the quantity of delivery vehicles sent to that facility to pickup shipments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a logical representation of the operations of amaterials handling facility, according to some embodiments.

FIG. 2 illustrates an example system configuration including a shipmentmanagement component, according to some embodiments.

FIG. 3 illustrates an example flow diagram for creating ship methodforecasts, according to some embodiments.

FIG. 4 illustrates example data structures utilized for creating shipmethod forecasts, according to some embodiments.

FIGS. 5A-5B illustrates a flowchart of an example method generating aship method forecast during periods of peak shipment output, accordingto some embodiments.

FIG. 6 illustrates a flowchart of an example method for generating aship method forecast for a distant period of time, according to someembodiments.

FIG. 7 illustrates one example of a computer system suitable forimplementing various elements of the system and method for generatingshipment forecasts for materials handling facilities, according to someembodiments.

While the system and method for generating shipment forecasts formaterials handling facilities is described herein by way of example forseveral embodiments and illustrative drawings, those skilled in the artwill recognize that the system and method for generating shipmentforecasts for materials handling facilities is not limited to theembodiments or drawings described. It should be understood, that thedrawings and detailed description thereto are not intended to limit thesystem and method for generating shipment forecasts for materialshandling facilities to the particular form disclosed, but on thecontrary, the intention is to cover all modifications, equivalents andalternatives falling within the spirit and scope of the system andmethod for generating shipment forecasts for materials handlingfacilities as defined by the appended claims. The headings used hereinare for organizational purposes only and are not meant to be used tolimit the scope of the description or the claims. As used throughoutthis application, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include,” “including,” and“includes” mean including, but not limited to.

DETAILED DESCRIPTION OF EMBODIMENTS

Various embodiments of a system and method for generating shipmentforecasts for materials handling facilities are described. FIG. 1illustrates a logical representation or view of the operation of amaterials handling facility 100 of various embodiments of the system andmethod for generating shipment forecasts for materials handlingfacilities. In various embodiments, a fulfillment network includingmultiple materials handling facilities (each of which may be configuredin a manner similar to that of materials handling facility 100) may beresponsible for fulfilling multiple orders, such as orders placedthrough an electronic commerce (“e-commerce”) portal.

In various embodiments, a materials handling facility may include one ormore facilities that process, store, and/or distribute units of itemsincluding but not limited to warehouses, distribution centers, hubs,fulfillment centers, nodes in a supply chain network, retailestablishments, shipping facilities, stock storage facilities, or anyother facility configured to process units of items. For example, thisFigure may illustrate an order fulfillment center of a productdistributor, according to some embodiments. Multiple customers 10 maysubmit orders 20 to the product distributor through an e-commerce portalor other electronic marketplace, where each order 20 specifies one ormore items from inventory 30 to be shipped to the customer thatsubmitted the order. To fulfill the customer orders 20, the one or moreitems specified in each order may be retrieved, or picked, frominventory 30 (which may also be referred to as stock storage) in thematerials handling facility, as indicated at 40. Picked items may bedelivered or conveyed, if necessary, to one or more stations in thematerials handling facility for sorting 50 into their respective orders,packing 60, and finally shipping 70 to the customers 10. In variousembodiments, picked items may be delivered to a station where individualunits of items are associated with and placed into particular conveyancereceptacles, which are then inducted into a conveyance mechanism. Theconveyance receptacles may then be routed to particular destinations forthe items contained within the receptacles in accordance with therequests (orders) currently being processed, e.g. to sorting stations,under direction of a control system (e.g., control system 102). Apicked, packed and shipped order does not necessarily include all of theitems ordered by the customer; an outgoing shipment to a customer mayinclude only a subset of the ordered items available to ship at one timefrom an inventory storage location.

A materials handling facility may also include a receiving 80 operationfor receiving shipments of stock (e.g., units of inventory items) fromone or more sources (e.g., vendors) and for moving or “stowing” thereceived stock into stock storage (e.g., inventory 30). The receiving 80operation may also receive and process returned purchased or renteditems or orders from customers. At least some of these items aretypically returned to inventory 30. The various operations of amaterials handling facility may be located in one building or facility,or alternatively may be spread or subdivided across two or morebuildings or facilities. In various instances, it should be understoodthat references to elements, units, items, processes (or anything else)as being located within materials handling facility 100 may easily beextended to encompass elements, units, items, processes (or anythingelse) proximate to but not physically located within materials handlingfacility. For example, various elements, units, items, or processes (oranything else) may be implemented outside of the materials handlingfacility, according to some embodiments.

In various embodiments, shipments of one or more items at shipping 70may be transferred to one or more shipment carrier network(s) 75. Eachshipment carrier's network may include one or more distributionfacilities for storing items (e.g., hubs) as well as vehicles forconveying shipments (e.g., trucks) from such distribution facilitiesand/or materials handling facilities (such as materials handlingfacility 100) to various destinations (e.g., customer specifieddestinations).

While embodiments presented herein are largely described with respect toa single material handling facility (such as materials handling facility100), it should be understood that embodiments may be configured togenerate shipment forecasts for any materials handling facility in anetwork of multiple materials handling facilities. For instance, variousembodiments may be configured to generate shipment forecasts formaterials handling facilities in a network of many geographicallydiverse materials handling facilities.

FIG. 2 illustrates a system configuration for implementing embodimentsof the system and method for generating shipment forecasts for materialshandling facilities. In various embodiments, any of the illustratedelements may be fully or partially implemented on a computer system,such as computer system 700 of FIG. 7. Embodiments may include aforecasting component 200, which may be configured to generate ashipment forecast that specifies, for one or more periods in time (e.g.,one or more days), a quantity of shipments that are projected to beshipped from a materials handling facility, such as materials handlingfacility 100 described above. Such forecast may also specify a shipmentmethod that is to be utilized to ship a respective quantity ofshipments. In various embodiments, a shipment method may be specific toa particular shipment carrier. For instance, one non-limiting example ofa shipment forecast for a particular day may specify that 1000 shipmentsare to be processed by a first shipping company's ground shippingservice and that 2000 shipments are to be processed by the firstshipping company's two-day air service. In this non-limiting example,that same shipment forecast may also specify that 3000 shipments are tobe processed by a second shipment carrier's overnight air service andthat 500 shipments are to be processed by the second shipment carrier'sground service. As described herein, a given shipment method maygenerally be specific to a particular shipment carrier as well as aparticular type of service provided by that particular shipment carrier(e.g., overnight shipping, second day shipping, ground shipping, etc.).(Although this need not be the case in all embodiments.)

Forecasting component 200 may utilize various techniques to generate aforecast, examples of which are described in more detail below. Invarious embodiments, forecasting component 200 may be configured togenerate shipping forecasts based (at least partially) on historicalshipment data, such as the historical shipment data stored in historicalshipment data store 210. Historical shipment data store 210 may invarious embodiments store information that indicates, for any timeperiod in the past, multiple shipment methods that were utilized to shipshipments from a respective materials handling facility as well as thequantity of shipments that were shipped according to each of suchshipment methods. In various embodiments, historical shipment data store210 may also store information that indicates, for any time period inthe past, multiple shipment service levels (e.g., 2-day shipping, groundshipping, economy shipping, etc.) as well as the quantity of shipmentsthat were shipped according to each of such shipment service levels. Asdescribed in more detail below, shipment methods may be carrier specificwhereas shipment service levels are not necessarily carrier specific.

In various embodiments, forecasting component 200 may be configured tocommunicate data or information to/from any illustrated element over oneor more network(s) 220, which may be configured in manner similar tothat of network 785 of FIG. 7 described below. In general, anyillustrated element of FIG. 2 may be configured to communicate data orinformation to/from any other illustrated element. It should beunderstood that when a given element is described as receivinginformation provided by another element, such exchange may occur overone or more network(s) 220.

Supply chain interface 230 may be configured to provide projections offacility output to forecasting component 200. In one non-limitingexample, supply chain interface 230 may provide weekly or daily datathat specifies a projected output of a given materials handlingfacility. For a given facility, this output may be expressed in totalunits expected to be shipped per day, total units expected to be shippedper week, or total units expected to be shipped per some other timeperiod, for example. As described in more detail below, forecastingcomponent 200 may be configured to utilize the information provided bysupply chain interface 230 in order to scale initial forecasts generatedfrom historical data. In various embodiments, the projected outputinformation may be generated based on a variety of information, such asestimates or predictions based on historical sales data or predictionsas to future sales. While various embodiments describe supply chaininterface 230 as providing the above-described information toforecasting component 200, it should be understood that in someembodiments forecasting component 200 may receive such information fromother sources or may generate such information independently.

Transportation simulation engine 240 may be configured to, for a givenshipment (either an actual shipment or a hypothetical shipment) to beshipped from a materials handling facility, identify the shipment method(e.g., carrier and service) that is to be utilized to ship thatshipment. The transportation simulation engine 240 may identify suchshipment method according to a variety of techniques including but notlimited to evaluating the shipment data (e.g., shipment service levelselected for the shipments, shipment dimensions, shipment weight,shipment origin, shipment destination, etc.) and/or evaluatinginformation of various shipment carriers (e.g., carriers services,shipment costs, historical shipping performance data from data store210, etc.). Additional description as to the manner in which simulationengine 240 selects a particular shipment method for a given shipment isdescribed below with respect to later figures.

Transportation simulation engine 240 may be configured to perform theabove-described analysis on multiple shipments. For example, in someembodiments, transportation simulation engine 240 may evaluate a sampleof shipments from a population of historical shipments (e.g., shipmentsfrom one or more previous weeks or years) from historical shipment datastore 210. The results of this sample-based analysis may form adistribution of multiple different shipment methods. In one non-limitingexample, such distribution may be a frequency distribution thatspecifies, for each of multiple shipment methods, the quantity ofshipments that were assigned that shipment method by transportationsimulation engine 240. In various embodiments, such a distribution maybe provided to forecasting component 200. As described in more detailbelow, forecasting component 200 may utilize such a distribution tocreate a forecast of the shipment methods that will be utilized to shipmultiple shipments.

Forecasting component 200 may be configured to provide such forecast toshipment carrier system(s) 250, each of which may be controlled and/oraccessible by a respective shipment carrier. By providing a shipmentcarrier with a forecast specifying shipment methods and quantities ofshipments to be shipped according to those shipment methods, embodimentsmay make it easier for shipment carriers to plan for future work. Forexample, in some cases, shipment carriers may use distinct vehicles fordifferent shipments that are picked up from a materials handlingfacility. For instance, if a shipment carrier is given an accurate ornear-accurate daily estimate of the shipments that will need to beshipped and the corresponding shipment methods for those shipments, theshipment carrier may ensure that the proper delivery vehicles (andproper quantities of such vehicles) are dispatched to the materialshandling facility to pick up shipments.

Forecasting component 200 may also be configured to provide the forecastto operations management interface 260. In various embodiments,operations management interface 260 may be any computer or electronicdevice (or some combination thereof) configured to receive and provideinformation from forecasts and provide such information to one or moreusers, such as management entities (e.g., agents). In one example, aforecast may be provided to an operations management interface, whichmay display information from the forecast as a graphical representationon an electronic display accessible to a management entity. In somecases, one or more management interfaces 260 may be included within amaterials handling. For instance, a management entity within thematerials handling facility may view information from the forecast andperform decisions or tasks based on such information. In onenon-limiting example, a management entity responsible for staffingagents at different areas or stations within the materials handlingfacility may select the locations to which agents are staffed at leastin part based on a forecast obtained through an operations managementinterface. For instance, certain packing and/or preparations stationswithin the materials handling facility may be associated with expeditedshipping methods (e.g., overnight air shipping). Based on the number ofshipments to be shipped according to that shipping method (as specifiedby the forecast), management entities may determine the quantity ofagents that should be staffed at those stations. In yet otherembodiments, at least some staffing levels within the materials handlingfacility may be controlled by automated systems (e.g., a computerconfigured to automatically assign agents to work stations). In thesecases, the forecast component 200 may be configured to provide generatedforecasts to such automated systems, which may then automaticallycontrol staffing levels at various work stations based on the forecastsand without user intervention (or with minimal user intervention). Itshould be understood that, in other cases, the forecasts generated byforecast component 200 may be provided to other systems and/or utilizedfor other purposes than those described herein.

FIG. 3 illustrates an example data flow diagram that represents thegeneration of a ship method forecast, according to some embodiments.FIG. 4 illustrates non-limiting examples of the data elements describedwith respect to FIG. 3. FIG. 3 and FIG. 4 may be collectively describedherein. It should be noted that the description of FIGS. 3 and 4 largelypertains to generating a ship method forecast specific to a particularmaterials handling facility. However, various embodiments may includeperforming multiple instances of the ship method forecast generation formultiple different materials handling facilities. Additionally, portionsof ship method forecast generation may be described with respect to aparticular time period (e.g., a single day); however, it should beunderstood that the techniques described herein may be performed formultiple time periods, such as a series of multiple days. Furthermore,while the granularity of the time periods is largely described at thelevel of a single day, it should be understood that the techniquesdescribed herein may be extended to encompass granularities of largertime periods (e.g., a week, month, year, etc.) as well as smaller timeperiods (e.g., hours, minutes, seconds, etc.). In the non-limitingexamples of FIGS. 3 and 4, a ship method forecast 310 is specific to aparticular materials handling facility and a particular day.

As demonstrated by the illustrated embodiment, preliminary forecastgenerator 202, scaling component 204 and distribution applicationcomponent 206 may be components of forecasting component 200. In othercases, these components may be implemented as components distinct fromforecasting component 200. Similarly, while supply chain interface 230and transportation simulation engine 240 are illustrated as componentsdistinct from forecasting component 200, these components may beimplemented as part of forecasting component 200 in some embodiments.

As illustrated by historical shipment data 300, preliminary forecastgenerator 202 may be configured to receive historical shipment data fromhistorical shipment data store 210. In various embodiments, historicalshipment data may specify different shipment service levels as well asthe quantity of shipments shipped according to those shipment servicelevels over time. In various embodiments, shipment service levels may beservice levels offered to a customer, such as a customer that purchasesan item to be shipped (e.g., an item purchased via an e-commerceportal). In one non-limiting example, a customer (or some other user)may specify a shipment service level at checkout time when ordering oneor more items to be shipped. Examples of shipment service levels includeovernight delivery (e.g., one-day delivery), second day delivery, threeday guaranteed delivery, or economy shipping (e.g., ground shipping). Invarious embodiments, these shipment service levels may be provided atdifferent costs to the consumer. For instance, an overnight shipmentservice level (e.g., overnight air shipping) may be more expensive thanan economy shipment service level (e.g., free ground shipping). In someembodiments, shipment service levels may primarily differ by the time ordeadline by which delivery of a respective shipment is promised.However, in some cases, shipment service levels may differ in otherrespects. For instance, in one embodiment, a “white glove” shipmentservice may differ from other shipment service levels by the manner inwhich shipments are to be handled. For instance, shipments (e.g.,shipments including fragile or bulky items) shipped according to therequirements of a “white glove” service level may be handled with extracare or may include other services such as in-home installation.

One example of historical shipment data is illustrated by historicalshipment data 400. In the illustrated embodiment, historical shipmentdata 400 includes a visual representation of the quantity of shipmentsshipped by a particular materials handling facility (e.g., materialshandling facility 100) over period of time (e.g., over multiple days).In the example historical shipment data 400, a distinct set ofhistorical shipment data is provided for each of multiple shipmentservice levels. The example shipment service levels include 2nd daydelivery, economy, three day guaranteed and overnight. It should beunderstood that, in other cases, more, less, and/or different shipmentservice levels may be utilized. In various embodiments, historicalshipment data may be received for any time period, such as time periodfrom a previous week, month or year. Also note that while historicalshipment data 400 is illustrated as graphical data, the same or similardata may be represented in tabular form or some other non-graphicalmanner.

In various embodiments, shipment service levels offered to customers maynot be specific to any particular shipment carrier and/or shipmentmethod provided by such carrier. For instance, a shipment that is to beshipped according to an overnight shipment service level may be shippedby multiple alternative shipment methods, such as a first shipmentcarrier's overnight air service, a second carrier's overnight airservice, or even a ground shipping service of one of the carrier's.Unlike shipment service levels, a given shipment method may in variousembodiments be specific to a shipment carrier. For instance, a firstshipment carrier's overnight air service may be a different shipmentmethod than a second carrier's overnight air service even though suchservices may be similar in nature.

Preliminary forecast generator 202 may be configured to apply any of avariety of techniques to historical shipment data 300 in order togenerate a preliminary forecast 302. In various embodiments, preliminaryforecast 302 may specify, for each of the shipment service levelsoffered by the materials handling facility for which the forecast isbeing generated, the quantity of shipments to be shipped from thematerials handling facility according to that shipment service levelduring a particular time period. In the illustrated embodiment, thistime period may be a particular day. In other embodiments, othergranularities of time may be utilized (e.g., a week, month, etc.).

In some embodiments, preliminary forecast generator 202 may beconfigured to utilize a time series forecasting model to generatepreliminary forecast 302. In one non-limiting example, preliminaryforecast generator 202 may be configured to apply the Add-Winters timeseries forecasting model to historical shipment data 300 in order togenerate preliminary forecast 302.

One example of preliminary forecast 302 is illustrated as preliminaryforecast 402, which may be a preliminary forecast for a particularoperating day of the materials handling facility for which the forecastis being generated. In the illustrated embodiments of FIG. 4, note thatshipment service level is denoted as “SSL.” Preliminary forecast 402specifies, for each of four different shipment service levels, thecorresponding quantity of shipments that are projected to be shipped bythe materials handling facility in accordance with that shipment servicelevel. As illustrated, these four shipment service levels include anovernight shipment service level, a second day delivery shipment servicelevel, a three day guaranteed shipment service level as well as aneconomy shipment service level. It should be understood that, in otherembodiments, different shipment service levels and/or differentquantities of shipment service levels may be utilized. In theillustrated example, preliminary forecast 402 may indicate that 1000overnight shipments are projected to be shipped from the materialshandling facility on a particular day, 4000 second day deliveryshipments are projected to be shipped from the materials handlingfacility on the particular day, 2000 three day guaranteed shipments areprojected to be shipped from the materials handling facility on theparticular day, and that 3000 economy shipments are projected to beshipped from the materials handling facility on the particular day.

Preliminary forecast generator 202 may be configured to providepreliminary forecast 302 to scaling component 204. Scaling component 204may be configured to modify the preliminary forecast based on projectedfacility output 304. For instance, forecasting component 200 may beconfigured to receive projected facility output 304 from supply chaininterface 230. In various embodiments, the projected facility output maybe generated based on a variety of information, such as estimates orpredictions based on historical sales data or predictions as to futuresales. In general, projected facility output 304 may specify total oraggregate output of the materials handling facility for the particulartime period being evaluated (e.g., for a particular business day orseries of days). In various embodiments, projected facility output 304may be specified in terms of units (e.g., units of items purchased bycustomers) or in terms of shipments (e.g., packages including one ormore units). In cases where projected facility output 304 is expressedin terms of units, forecasting component may convert the projectedfacility output such that it is expressed in terms of packages (thisprocess is described below with respect to later figures).

One non-limiting example of projected facility output 304 is illustratedas projected facility output 404. In the illustrated embodiment, theprojected facility output specifies the materials handling facility forwhich the projected output applies. In this example, the data applies to“materials handling facility 1” (denoted as MHF1). The projected outputfor that facility is specified as 11,000 shipments. (In other cases,this value may be expressed in terms of units instead of shipments.) Theillustrated data may specify that MHF1 is projected to ship 11,000shipments total on a particular day (or week, or some other timeperiod).

Scaling component 204 may be configured to utilize the projectedfacility output to modify preliminary forecast 302. In variousembodiments, scaling component 204 may proportionately modify or scalethe forecasted values of preliminary forecast 302 such that the sum ofthe forecasted values is equivalent to the sum of projected facilityoutput 304. For example, the sum of the projected shipments ofpreliminary forecast 402 is 10,000 shipments; the projected facilityoutput 404 is 11,000 shipments (i.e., 10% larger than the sum of theprojected shipments of preliminary forecast 402). In order toproportionately modify the values of the preliminary forecast such thatthe sum of such values equals the projected facility output, scalingcomponent 204 may be configured to increase each individual value of thepreliminary forecast by 10%. In this way, the total quantity ofshipments of the preliminary forecast may change (e.g., to match theprojected facility output) while the projected shipments of eachshipment level make up the same proportion of the total shipments asprior to scaling. The result of the above-described process isillustrated by scaled forecast 406. Note that each shipment servicelevel's projection has been proportionately scaled by 10%. It should beunderstood that, in other embodiments, these values may be scaleddifferently dependent upon the preliminary forecast and/or the projectedfacility output.

The result of scaling the preliminary forecast is illustrated as scaledforecast 306. This forecast may in various embodiments be similar instructure to the preliminary forecast. The primary difference in someembodiments may be that the values of the scaled forecast have beenscaled as described above. One example of a scaled forecast isillustrated as scaled forecast 406, the values of which each have beenscaled upward by 10% (because the projected facility output is 10%larger than the sum of the preliminary forecast values). In variousembodiments, scaling component 204 may provide scaled forecast 306 todistribution application component 206. Distribution applicationcomponent 206 may be configured to apply projected shipment methoddistribution(s) 308 to scaled forecast 306 in order to generate shipmethod forecast 310.

Forecasting component 200 may be configured to receive one or moreshipment method distributions 308 from transportation simulation engine240. As described above, transportation simulation engine 240 may beconfigured to, for a given shipment (either an actual shipment or ahypothetical shipment) to be shipped from the materials handlingfacility, identify the shipment method (e.g., carrier and service) thatis to be utilized to ship that shipment. The transportation simulationengine 240 may identify such shipment method according to a variety oftechniques including but not limited to evaluating the shipment data(e.g., shipment service level selected for the shipments, shipmentdimensions, shipment weight, shipment origin, shipment destination,delivery deadline, etc.) and/or evaluating information of variousshipment carriers (e.g., e.g., carriers services, shipment costs,historical shipping performance of the carrier, etc.).

In various embodiments, transportation simulation engine 240 may beconfigured to store historical shipment carrier performance data. Invarious embodiments, historical shipment carrier performance data mayspecify, for a given type of shipment, a historical (e.g., average ormedian) transit period for delivering that type of shipment; theshipment type may be defined by shipment weight, dimensions, origin,destination, some other metadata about the shipment, or some combinationthereof. In one non-limiting example, one type of shipment may be asmall, medium-weight shipment originating in one zip code and destinedfor another zip code. The carrier performance data may specify, for agiven carrier, historical transit times for delivering that type ofshipment according to any of that carrier's shipment methods (e.g.,overnight air, ground, etc.). In various embodiments, for a givenshipment, transportation simulation engine 240 may select a shipmentmethod (e.g., a combination of shipment carrier and shipment serviceprovided by that carrier) capable of providing the shipment to itsdestination by the designated delivery deadline as indicated by thecarrier performance data for that type of shipment. In cases wheremultiple shipment methods meet this criteria, the transportation mayselect one of such shipment method based on one or more criteria, suchas cost. For example, when multiple shipment methods are suitable forthe shipment, the transportation simulation engine 240 may select theshipment method that costs the least (e.g., least cost incurred by themerchant that operates the materials handling facility).

In various embodiments, costs and/or other variables of differentshipment methods may change over time (e.g., due to contractnegotiations between the merchant and shipment carriers). Transportationsimulation engine 240 may base its operation on the most recentinformation for the possible shipment methods that may be selected for ashipment. Similarly, carrier availability and/or shipment methodsoffered by carriers may change over time. The information on whichtransportation simulation engine 240 bases its decisions may includeup-to-date versions of carrier availability and/or shipment methods.Accordingly, for two shipments having the same parameters (e.g., weight,dimensions, destination, location, shipment service level, etc.) butshipped at different instances in time (e.g., one to be shipped a weekbefore the other), the transportation simulation engine 240 may provideselect different shipment methods for such shipments.

The transportation simulation engine 240 may apply the above-describedfunctionality to a sample of multiple shipments. In some embodiments,this sample may be selected from a population of historical shipments(e.g., shipments from a prior week, month, or year). In one non-limitingexample, transportation simulation engine may perform a simulation on5,000 historical shipments. When performing a simulation, transportationsimulation engine may apply the above-described techniques to eachshipment of the sample in order to generate projected ship methoddistribution(s) 308. In various embodiments, transportation simulationengine may perform a simulation and generate a corresponding projectedship method distribution 308 for each shipment service level describeabove.

Projected ship method distribution 408 may be one example of projectedship method distribution 308. As described above, such a distributionmay be generated for each shipment service level. In variousembodiments, the illustrated projected ship method distributioncorresponds to the “overnight” shipment service level of scaled forecast406. Similar distributions may be created for each of the other shipmentservice levels. However, for clarity of illustration, thesedistributions are not illustrated. In the illustrated embodiment,projected ship method distribution 408 specifies which ship methods arerecommended for the 1100 overnight shipments of scaled forecast 406. Forinstance, the distribution specifies that 36.4% of the “overnight”shipment service level shipments are recommended to be sent by overnightair service provided by shipment carrier 1 (denoted as “SC1”).Similarly, 45.5% of the shipments are recommended to be shipped by“overnight air by 3 pm” service provided by shipment carrier 2 (denotedas “SC2”), 8% of the overnight shipment service level shipments arerecommended to be shipped by SC1 's ground service, 9% of the overnightshipment service level shipments are recommended to be shipped by SC2'sground service, and 1.1% of the overnight shipment service levelshipments are to be shipped by ground service of shipment carrier 3(denoted as “SC3”).

In various embodiments, distribution application component 206 may beconfigured to generate ship method forecast 310 by applying projectedship method distribution(s) 308 to scaled forecast 306. In variousembodiments, this may include distribution application component 206determining the quantity of shipment that are projected to be shipped byeach shipment method by multiplying the distribution percentage of thatship method by the corresponding value from the scaled forecast. Forexample, for projected ship method distribution 408, the quantity ofshipments to be processed by the “SC1 Overnight Air” ship method may bedetermined by multiplying 36.4% (or 0.364) by 1100 shipments (thecorresponding value of scaled forecast 406). In this example, the resultis 400 shipments. This process may be repeated for each projected shipmethod distribution and each ship method specified by thosedistributions. The distribution application component 206 may aggregate(e.g., via summation) these results to create ship method forecast 310.One example of ship method forecast 310 is illustrated as ship methodforecast 410. In the illustrated embodiment, ship method forecastspecifies multiple ship methods that are projected to be utilized in agiven day as well as respective quantities of shipments that areprojected to be processed by each shipment method. As illustrated, theshipment forecasts generated by the forecasting component 200 may beprovided to one or more shipment carriers and/or one or more operationsmanagement interfaces, such as those described above with respect toFIG. 2. As described above, shipment carriers and/or management entitiesof materials handling facilities may use such forecasts for planningpurposes (e.g., to plan for needed quantities of vehicles or personnel).

Example Methods

Embodiments of the system and method for generating shipment forecastsfor materials handling facilities may include various methods, such asthat of FIGS. 5-6 described below. In various embodiments, methods maybe implemented on a computer system, such as that of FIG. 7 describedbelow. In various embodiments, the methods described herein may beperformed by forecasting component 200 described above, which may alsobe implemented on a computer system, such as that of FIG. 7 describedbelow.

FIG. 5A illustrates a flowchart of an example method for generating shipmethod forecasts, according to some embodiments. In some cases, theillustrated method may be utilized during periods of high shipmentvolume (e.g., December). As illustrated by block 500, the method mayinclude applying a time series forecasting model to historical shipmentdata to create a preliminary forecast. One example of historicalshipment data is described above with respect to historical shipmentdata 300 and 400. For instance, this may include utilizing thetechniques described above with respect to the preliminary forecastgenerator in order to apply a time series forecasting model tohistorical shipment data. In some cases, prior to applying the model tothe historical data, the historical data may be conditioned to removeirregularities, such as irregularities due to holidays or other days inwhich shipment activity of a materials handling facility is artificiallylow. For instance, historical shipment data corresponding to a firstweek that includes a holiday may be substituted with data of anotherweek that is temporally proximate to that first week (e.g., the weekpreceding the holiday week). The result of block 500 may be apreliminary forecast, such as preliminary forecasts 302 and 402described above.

As illustrated by block 502, the method may include modifying thepreliminary forecast according to order cutoff dates. In variousembodiments, an order cutoff date may be the date at which customers mayno longer use a particular shipment service level (e.g., groundshipping) to receive a shipment by a given priority date (e.g., the daypreceding a major holiday). In some cases, historical trends mayindicate that order activity for shipments shipped according to aparticular shipment service level may peak on such cutoff dates.Accordingly, this portion of the method may modify the preliminaryforecast to increase volumes of shipments sent according to thatparticular shipment service level on a cutoff date.

As illustrated by block 504, the method may include modifying thepreliminary forecast according to a materials handling outputprojection. One example of such a projection is described above withrespect to projected facility output data 304 and 404. This portion ofthe method may include utilizing the techniques described above withrespect to the scaling component. For instance, the method may includeproportionately modifying or scaling the values of the preliminaryforecast such that the sum of such values is the same as the projectedfacility output. One example of this technique is described above withrespect to generating scaled forecast 406 from preliminary forecast 402and projected facility output 404. In some embodiments, the projectedfacility output may be expressed in terms of units instead of packages.In these cases, the method may include converting the facility outputvalue from units to packages. For instance, the method may includedividing the output value (in units) by a historical average quantity ofunits known to reside in the packages processed by the relevantmaterials handling facility. For instance, if the projected facilityoutput value were 10,000 units and the historical average number ofunits per package were 2, the method may include dividing the units perpackage value into the projected facility output value to determine thatthe projected facility output in terms of packages is 500.

As illustrated by block 506, the method may include separating highpriority shipment volume from lower priority shipment volume. In variousembodiments, different shipments may have different shipment deliverydeadlines. In some cases, the method may include designating shipmentsthat are to be delivered by a certain deadline (e.g., the day before amajor holiday) as being high priority shipments whereas shipments thatare to be delivered after that deadline are designated with a lowerpriority.

For high priority shipment volume, as illustrated by block 508 a, themethod may include applying a projected ship method distribution tothose shipments. This process may be similar to the techniques performedby distribution application component 206 described above (e.g.,utilizing a simulation to generate such distribution as well as applyingsuch distribution). For lower priority shipment volume, as illustratedby block 508 b, the method may include applying a historical ship methoddistribution to those shipments. This historical ship methoddistribution may be determined by determining the different shipmentmethods utilized by the materials handling facility during a past periodof time (e.g., a week occurring one month ago). While determined in adifferent manner, this distribution may be similar to projected shipmethod distributions 308 and 408 described above. Both 508 a and 508 bmay result in ship method forecasts similar to ship method forecasts 310and 410 described above. As illustrated by block 510, these forecastsmay be aggregated (e.g., combined) to generate the complete ship methodforecast.

In various embodiments, the method may include adjusting the ship methodforecast for capacity limitations if necessary (e.g., if the ship methodforecast suggests a ship method be utilized beyond its capacity). Forinstance, if a ship method forecast projects that 10,000 shipments areto be shipped according to a particular shipment carrier's groundshipment method and the threshold capacity of that shipment method isonly 9,000 shipments, the method may include assigning 9,000 shipmentsto that shipment method and performing blocks 508 a-512 again withoutconsideration of those 9,000 shipments nor the shipment method to whichthey are assigned (as illustrated at 514). In this way, the remaining1,000 shipments may be assigned to another shipment method. The newforecast generated by repeating blocks 508 a-512 may be aggregated withthe information that specifies the 9,000 shipments are assigned to theircorresponding ship method. This aggregation may result in an updatedshipment method forecast similar to those described above.

FIG. 5B illustrates a method similar to that of FIG. 5A that may beextended to any number of distinct portions of shipment volume. In FIG.5B, items 500-504 may be similar to those described above with respectto FIG. 5A. In various embodiments, the method may include separatingshipment volume into multiple distinct portions (block 506). In someembodiments, this portion of the method may include separating shipmentvolume on the basis of priority designation (e.g., high priority, lowpriority, etc.), shipment origin, shipment destination, or any othershipment characteristics. As illustrated by 508 a-n, the method mayinclude applying a ship method distribution to each different portion ofshipment volume. In some cases, some or all of the ship methoddistributions may be the same. Likewise, in some cases, some or all ofthe ship method distributions may be different. In various embodiments,some of the ship method distributions may be projected or forecasteddistributions, such as those generated by transportation simulationengine 240 described above. In some cases, some of the ship methoddistributions may be historical distributions based on historical data,such as data from historical shipment data store 210. In general, anytype of ship method distribution described herein may be applied to anygiven portion of shipment volume in various embodiments. The portions ofthe method corresponding to blocks 510-514 may be similar to thosedescribed above with respect to FIG. 5A. While not illustrated, thetechniques described above with respect to FIG. 5B may also be appliedto the example method of FIG. 6 described below.

FIG. 6 illustrates a flowchart of an example method for generating shipmethod forecasts, according to some embodiments. In some cases, theillustrated method may be utilized to generate long-term forecasts. Asillustrated by block 600, the method may include generating apreliminary forecast from historical data. For example, the method mayinclude determining the percentages of shipment service levelutilization during a particular month of a previous year. Note that invarious embodiments, this technique may be utilized instead of the timeseries forecast model described above. In various embodiments, theresult of block 600 may be similar to preliminary forecast 302 and 402described above, with the exception that shipment forecasts may beexpressed as percentages instead of absolute quantities. For instance,1,000 overnight shipments out of 10,000 total shipments would instead byexpressed as 10% of the overall shipment service levels, according tosome embodiments.

In cases where there is not sufficient historical data available onwhich to base the creation of a preliminary forecast (e.g., in caseswhere a relatively new materials handling facility is being evaluated),the method may include utilizing historical data about a similarmaterials handling facility in order to perform the method. Forinstance, a similar materials handling facility may be identified basedon one or more criteria (e.g., location, unit or shipment throughput,etc.); the historical data for that materials handling facility may beutilized to perform the method in some embodiments.

As illustrated by block 602, the method may include modifying thepreliminary forecast according to order cutoff dates, holidays, and/orweekends. Modifying the data according to cutoff dates and/or holidaysmay be performed in manner similar to that described above with respectto FIG. 5A. To adjust for weekends, the method may include shifting thehistorical data evaluated by one or two days to avoid evaluating days onwhich the materials handling facility is not operating (e.g., weekends).

As illustrated by block 604, the method may include modifying thepreliminary forecast based on changes in historical shippingpreferences. For example, if the two-day shipment service level (or anyother shipment service level) is currently 10% more popular thanprevious years, the method may include adjusting the preliminaryforecast to reflect such shift in customer preferences. For instance,the value corresponding to the two-day shipment service level in thepreliminary forecast may be increased by 10% in order to take such shiftinto consideration.

As illustrated by block 606, the method may include modifying thepreliminary forecast according to a materials handling facility outputprojection. This portion of the method may be performed in a mannersimilar to that described above with respect to FIG. 5A. Similarly, theseparation of high priority volume and lower priority volume, asillustrated at block 608, may also be performed in a manner similar tothat described above with respect to FIG. 5A.

As illustrated by blocks 610 a and 610 b, the method may includeseparately applying ship method distributions to both high priorityshipment volume and lower priority shipment volume. In variousembodiments, applying ship method distributions to either volume mayinclude performing techniques similar to those described above withrespect to distribution application component 206 and/or block 508 adescribed above. Both 610 a and 610 b may result in ship methodforecasts similar to ship method forecasts 310 and 410 described above.As illustrated by block 612, these forecasts may be aggregated (e.g.,combined) to generate the complete ship method forecast. As illustratedby blocks 614-616, the method may also include adjusting the ship methodforecast in accordance with any known ship method capacity limitation(similar to blocks 512-514 described above).

Example Computer System

Various embodiments of the system and method for generating shipmentforecasts for materials handling facilities, as described herein, may beexecuted on one or more computer systems, which may interact withvarious other devices. Note that any component, action, or functionalitydescribed above with respect to FIGS. 1-6 may be implemented via one ormore computer systems configured as computer system 700 of FIG. 7,according to various embodiments. In the illustrated embodiment,computer system 700 includes one or more processors 710 coupled to asystem memory 720 via an input/output (I/O) interface 730. Computersystem 700 further includes a network interface 740 coupled to I/Ointerface 730, and one or more input/output devices 750, such as cursorcontrol device 760, keyboard 770, and display(s) 780. In some cases, itis contemplated that embodiments may be implemented using a singleinstance of computer system 700, while in other embodiments multiplesuch systems, or multiple nodes making up computer system 700, may beconfigured to host different portions or instances of embodiments. Forexample, in one embodiment some elements may be implemented via one ormore nodes of computer system 700 that are distinct from those nodesimplementing other elements.

In various embodiments, computer system 700 may be a uniprocessor systemincluding one processor 710, or a multiprocessor system includingseveral processors 710 (e.g., two, four, eight, or another suitablenumber). Processors 710 may be any suitable processor capable ofexecuting instructions. For example, in various embodiments processors710 may be general-purpose or embedded processors implementing any of avariety of instruction set architectures (ISAs), such as the x86,PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. Inmultiprocessor systems, each of processors 710 may commonly, but notnecessarily, implement the same ISA.

System memory 720 may be configured to store program instructions 722and/or data 732 accessible by processor 710. In various embodiments,system memory 720 may be implemented using any suitable memorytechnology, such as static random access memory (SRAM), synchronousdynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type ofmemory. In the illustrated embodiment, program instructions 722implementing forecasting component 200 are shown stored within programinstructions 722. Additionally, data 732 of memory 720 may store any ofthe information or data structures described above, such as historicalshipment data 300 and/or ship method forecast 310. In some embodiments,program instructions and/or data may be received, sent or stored upondifferent types of computer-accessible media or on similar mediaseparate from system memory 720 or computer system 700. While computersystem 700 is described as implementing the functionality of forecastingcomponent 200, any of the components or systems illustrated above may beimplemented via such a computer system.

In one embodiment, I/O interface 730 may be configured to coordinate I/Otraffic between processor 710, system memory 720, and any peripheraldevices in the device, including network interface 740 or otherperipheral interfaces, such as input/output devices 750. In someembodiments, I/O interface 730 may perform any necessary protocol,timing or other data transformations to convert data signals from onecomponent (e.g., system memory 720) into a format suitable for use byanother component (e.g., processor 710). In some embodiments, I/Ointerface 730 may include support for devices attached through varioustypes of peripheral buses, such as a variant of the Peripheral ComponentInterconnect (PCI) bus standard or the Universal Serial Bus (USB)standard, for example. In some embodiments, the function of I/Ointerface 730 may be split into two or more separate components, such asa north bridge and a south bridge, for example. Also, in someembodiments some or all of the functionality of I/O interface 730, suchas an interface to system memory 720, may be incorporated directly intoprocessor 710.

Network interface 740 may be configured to allow data to be exchangedbetween computer system 700 and other devices (e.g., any other componentof the Figures described above) attached to a network 785 or betweennodes of computer system 700. Network 785 may in various embodimentsinclude one or more networks including but not limited to Local AreaNetworks (LANs) (e.g., an Ethernet or corporate network), Wide AreaNetworks (WANs) (e.g., the Internet), wireless data networks, some otherelectronic data network, or some combination thereof. In variousembodiments, network interface 740 may support communication via wiredor wireless general data networks, such as any suitable type of Ethernetnetwork, for example; via telecommunications/telephony networks such asanalog voice networks or digital fiber communications networks; viastorage area networks such as Fibre Channel SANs, or via any othersuitable type of network and/or protocol.

Input/output devices 750 may, in some embodiments, include one or moredisplay terminals, keyboards, keypads, touchpads, scanning devices,voice or optical recognition devices, or any other devices suitable forentering or accessing data by one or more computer systems 700. Multipleinput/output devices 750 may be present in computer system 700 or may bedistributed on various nodes of computer system 700. In someembodiments, similar input/output devices may be separate from computersystem 700 and may interact with one or more nodes of computer system700 through a wired or wireless connection, such as over networkinterface 740.

As shown in FIG. 7, memory 720 may include program instructions 722configured to implement any element or action described above. In oneembodiment, the program instructions may implement the methods describedabove, such as the methods illustrated by FIGS. 5-6. In otherembodiments, different elements and data may be included. Note that data732 may include any data or information described above.

Those skilled in the art will appreciate that computer system 700 ismerely illustrative and is not intended to limit the scope ofembodiments. In particular, the computer system and devices may includeany combination of hardware or software that can perform the indicatedfunctions, including computers, network devices, Internet appliances,PDAs, wireless phones, pagers, etc. Computer system 700 may also beconnected to other devices that are not illustrated, or instead mayoperate as a stand-alone system. In addition, the functionality providedby the illustrated components may in some embodiments be combined infewer components or distributed in additional components. Similarly, insome embodiments, the functionality of some of the illustratedcomponents may not be provided and/or other additional functionality maybe available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 700 may be transmitted to computer system700 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link. Various embodiments mayfurther include receiving, sending or storing instructions and/or dataimplemented in accordance with the foregoing description upon acomputer-accessible medium. Generally speaking, a computer-accessiblemedium may include a computer-readable storage medium or memory mediumsuch as magnetic or optical media, e.g., disk or DVD/CD-ROM, volatile ornon-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.),ROM, etc. In some embodiments, a computer-accessible medium may includetransmission media or signals such as electrical, electromagnetic, ordigital signals, conveyed via a communication medium such as networkand/or a wireless link.

The methods described herein may be implemented in software, hardware,or a combination thereof, in different embodiments. In addition, theorder of the blocks of the methods may be changed, and various elementsmay be added, reordered, combined, omitted, modified, etc. Variousmodifications and changes may be made as would be obvious to a personskilled in the art having the benefit of this disclosure. The variousembodiments described herein are meant to be illustrative and notlimiting. Many variations, modifications, additions, and improvementsare possible. Accordingly, plural instances may be provided forcomponents described herein as a single instance. Boundaries betweenvarious components, operations and data stores are somewhat arbitrary,and particular operations are illustrated in the context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within the scope of claims that follow. Finally,structures and functionality presented as discrete components in theexemplary configurations may be implemented as a combined structure orcomponent. These and other variations, modifications, additions, andimprovements may fall within the scope of embodiments as defined in theclaims that follow.

What is claimed is:
 1. A system, comprising: a memory; and one or moreprocessors coupled to the memory, wherein the memory comprises programinstructions executable by the one or more processors to implement aforecasting component configured to: for each of multiple differentshipment service levels offered to customers, generate a projection ofthe quantity of shipments that will be shipped from a materials handlingfacility according to that shipment service level during a particulartime period, wherein a different respective projection is generated foreach of the multiple different shipment service levels; based on anaggregate quantity of shipments projected to be shipped from thematerials handling facility during the particular time period, modifythe projected quantities of shipments for the multiple shipment servicelevels to provide a different respective modified projected quantity ofshipments for each shipment service level; for each shipment servicelevel, receive information specifying a distribution of differentshipment methods that are projected to be utilized during the particulartime period in order to meet requirements of that shipment servicelevel, wherein a different respective distribution is received for eachdifferent respective service level; and based on the respectivedistribution for each different respective shipment service level andthe respective modified projected quantity of shipments for eachshipment service level, generate a forecast, wherein said forecastspecifies respective quantities of shipments that are to be shippedduring the particular time period according to each of the differentshipment methods.
 2. The system of claim 1, wherein one or more of themultiple different shipment service levels include one or more of: aone-day shipment service level, a two-day shipment service level, or aneconomy shipment service level.
 3. The system of claim 1, wherein togenerate a given projection of the quantity of shipments that will beshipped from the materials handling facility according to a givenshipment service level, the forecasting component is configured to:utilize a time series forecasting model to generate the given projectionfrom historical data.
 4. The system of claim 1, wherein the forecastingcomponent is configured to generate said aggregate quantity of shipmentsprojected to be shipped from the materials handling facility, wherein togenerate said aggregate quantity of shipments, the forecasting componentis configured to: receive information specifying a historical average ofa quantity of units per shipment processed by the materials handlingfacility; receive information specifying an aggregate quantity of unitsprojected to be processed by the materials handling facility during theparticular time period; and divide said aggregate quantity of units bythe historical average of the quantity of units per shipment to generatesaid aggregate quantity of shipments.
 5. The system of claim 1, whereinto modify the projected quantities of shipments for the multipleshipment services levels based on said aggregate quantity of shipments,the forecasting component is configured to: proportionately modify theprojected quantities such that a summation of the projected quantitiesis equivalent to said aggregate quantity of shipments.
 6. The system ofclaim 1, wherein the distribution of different shipment methods for agiven shipment service level indicates, for each different shipmentmethod, a projection of a frequency with which that shipment method willbe utilized to ship shipments of the given shipment service levelrelative to a projection of the frequencies with which other shipmentmethods will be utilized to ship shipment of the given shipment servicelevel.
 7. The system of claim 1, wherein said distribution of differentshipment methods that are projected to be utilized comprises adistribution of different shipment methods utilized to ship shipments ofa particular priority designation, and wherein said respective forecastcomprises a forecast specific to the shipments of the particularpriority designation.
 8. The system of claim 1, wherein said multipledifferent shipment service levels are not specific to any particularshipment carrier, wherein at least some of said different shipmentmethods are specific to a respective shipment carrier.
 9. The system ofclaim 7, wherein the forecasting component is configured to: for aparticular shipment method, determine that said forecast specifies aquantity of shipments that exceeds a threshold capacity for thatshipment method; and generate a second forecast for the shipments of theparticular priority designation; wherein the generation of the secondforecast excludes from consideration, the particular shipment method andany shipments assigned to the particular shipment method.
 10. A method,comprising: performing, by one or more computing devices: for each ofmultiple different shipment service levels offered to customers,generating a projection of the quantity of shipments that will beshipped from a materials handling facility according to that shipmentservice level during a particular time period, wherein a differentrespective projection is generated for each of the multiple differentshipment service levels; based on an aggregate quantity of shipmentsprojected to be shipped from the materials handling facility during theparticular time period, modifying the projected quantities of shipmentsfor the multiple shipment service levels to provide a differentrespective modified projected quantity of shipments for each shipmentservice level; for each shipment service level, receiving informationspecifying a distribution of different shipment methods that areprojected to be utilized during the particular time period in order tomeet requirements of that shipment service level, wherein a differentrespective distribution is received for each different respectiveservice level; and; based on the respective distribution for eachdifferent respective shipment service level and the respective modifiedprojected quantity of shipments for each shipment service level,generating a forecast, wherein said forecast specifies respectivequantities of shipments that are to be shipped during the particulartime period according to each of the different shipment methods.
 11. Themethod of claim 10, wherein one or more of the multiple differentshipment service levels include one or more of: a one-day shipmentservice level, a two-day shipment service level, or an economy shipmentservice level.
 12. The method of claim 10, wherein generating a givenprojection of the quantity of shipments that will be shipped from thematerials handling facility according to a given shipment service levelcomprises: utilizing a time series forecasting model to generate thegiven projection from historical data.
 13. The method of claim 10,wherein the method comprises generating said aggregate quantity ofshipments projected to be shipped from the materials handling facility,wherein said generating comprises: receiving information specifying ahistorical average of a quantity of units per shipment processed by thematerials handling facility; receiving information specifying anaggregate quantity of units projected to be processed by the materialshandling facility during the particular time period; and dividing saidaggregate quantity of units by the historical average of the quantity ofunits per shipment to generate said aggregate quantity of shipments. 14.The method of claim 10, wherein modifying the projected quantities ofshipments for the multiple shipment services levels based on saidaggregate quantity of shipments comprises: proportionately modifying theprojected quantities such that a summation of the projected quantitiesis equivalent to said aggregate quantity of shipments.
 15. The method ofclaim 10, wherein the distribution of different shipment methods for agiven shipment service level indicates, for each different shipmentmethod, a projection of a frequency with which that shipment method willbe utilized to ship shipments of the given shipment service levelrelative to a projection of the frequencies with which other shipmentmethods will be utilized to ship shipment of the given shipment servicelevel.
 16. The method of claim 10, wherein said shipments of aparticular priority designation comprise shipments that are expected tobe delivered by a particular delivery deadline.
 17. The method of claim10, wherein said distribution of different shipment methods that areprojected to be utilized comprises a distribution of different shipmentmethods utilized to ship shipments of a particular priority designation,and wherein said respective forecast comprises a forecast specific tothe shipments of the particular priority designation.
 18. The method ofclaim 17, comprising: for a particular shipment method, determining thatsaid forecast specifies a quantity of shipments that exceeds a thresholdcapacity for that shipment method; and generating a second forecast forthe shipments of the particular priority designation; wherein thegeneration of the second forecast excludes from consideration, theparticular shipment method and any shipments assigned to the particularshipment method.
 19. A non-transitory computer-readable storage medium,storing program instructions computer-executable on a computer system toimplement a forecasting component configured to: for each of multipledifferent shipment service levels offered to customers, generate aprojection of the quantity of shipments that will be shipped from amaterials handling facility according to that shipment service levelduring a particular time period, wherein a different respectiveprojection is generated for each of the multiple different shipmentservice levels; based on an aggregate quantity of shipments projected tobe shipped from the materials handling facility during the particulartime period, modify the projected quantities of shipments for themultiple shipment service levels to provide a different respectivemodified projected quantity of shipments for each shipment servicelevel; for each shipment service level, receive information specifying adistribution of different shipment methods that are projected to beutilized during the particular time period in order to meet requirementsof that shipment service level, wherein a different respectivedistribution is received for each different respective service levelbased on the respective distribution for each different respectiveshipment service level and the respective modified projected quantity ofshipments for each shipment service level, generate a forecast, whereinsaid forecast specifies respective quantities of shipments that are tobe shipped during the particular time period according to each of thedifferent shipment methods.
 20. The non-transitory computer-readablestorage medium of claim 19, wherein one or more of the multipledifferent shipment service levels include one or more of: a one-dayshipment service level, a two-day shipment service level, or an economyshipment service level.
 21. The non-transitory computer-readable storagemedium of claim 19, wherein to generate a given projection of thequantity of shipments that will be shipped from the materials handlingfacility according to a given shipment service level, the forecastingcomponent is configured to: utilize a time series forecasting model togenerate the given projection from historical data.
 22. Thenon-transitory computer-readable storage medium of claim 19, wherein theforecasting component is configured to generate said aggregate quantityof shipments projected to be shipped from the materials handlingfacility, wherein to generate said aggregate quantity of shipments, theforecasting component is configured to: receive information specifying ahistorical average of a quantity of units per shipment processed by thematerials handling facility; receive information specifying an aggregatequantity of units projected to be processed by the materials handlingfacility during the particular time period; and divide said aggregatequantity of units by the historical average of the quantity of units pershipment to generate said aggregate quantity of shipments.
 23. Thenon-transitory computer-readable storage medium of claim 19, wherein tomodify the projected quantities of shipments for the multiple shipmentservices levels based on said aggregate quantity of shipments, theforecasting component is configured to: proportionately modify theprojected quantities such that a summation of the projected quantitiesis equivalent to said aggregate quantity of shipments.
 24. Thenon-transitory computer-readable storage medium of claim 19, wherein thedistribution of different shipment methods for a given shipment servicelevel indicates, for each different shipment method, a projection of afrequency with which that shipment method will be utilized to shipshipments of the given shipment service level relative to a projectionof the frequencies with which other shipment methods will be utilized toship shipment of the given shipment service level.
 25. Thenon-transitory computer-readable storage medium of claim 19, whereinsaid distribution of different shipment methods that are projected to beutilized comprises a distribution of different shipment methods utilizedto ship shipments of a particular priority designation, and wherein saidrespective forecast comprises a forecast specific to the shipments ofthe particular priority designation.
 26. The non-transitorycomputer-readable storage medium of claim 19, wherein said multipledifferent shipment service levels are not specific to any particularshipment carrier, wherein at least some of said different shipmentmethods are specific to a respective shipment carrier.
 27. Thenon-transitory computer-readable storage medium of claim 25, wherein theforecasting component is configured to: for a particular shipmentmethod, determine that said forecast specifies a quantity of shipmentsthat exceeds a threshold capacity for that shipment method; and generatea second forecast for the shipments of the particular prioritydesignation; wherein the generation of the second forecast excludes fromconsideration, the particular shipment method and any shipments assignedto the particular shipment method.